key: cord-0787526-6g15jtd2 authors: Ordu, Muhammed; Kirli Akin, Hediye; Demir, Eren title: Healthcare systems and Covid19: Lessons to be learnt from efficient countries date: 2021-05-02 journal: Int J Health Plann Manage DOI: 10.1002/hpm.3187 sha: 259ff36782f75a0e824cff1fb6d9faea425a8951 doc_id: 787526 cord_uid: 6g15jtd2 BACKGROUND: The novel coronavirus is rapidly spreading over the world and puts the health systems of countries under intense pressure. High hospitalization levels due to the pandemic outbreak have caused the intensive care units to work above capacity. PURPOSE: A data envelopment analysis (DEA) based modelling approach was developed to evaluate the effectiveness of regions (i.e. city, country or clinical commissioning groups) against the pandemic outbreak. The objective is to enable related authorities better manage the struggle against the outbreak and put in place the emergency action plans immediately. METHODOLOGY/APPROACH: DEA method was used to measure the efficiency scores of countries. Super efficiency DEA method was also applied to countries based on the level of efficiencies they have achieved. Sixteen countries were selected that have been facing with Covid19 pandemic outbreak for at least 5 consecutive weeks after their 100th confirmed case. RESULTS: A total of 80 DEA models were developed, that is, 16 DEA models for each week. The percentage of efficient countries decreased dramatically over time, from 43.75% in the first week to 25% in the fifth week. Unlike most European countries, China and South Korea increased their effectiveness after first week of implementing all the necessary measures. CONCLUSION: This study sheds light into better understanding the effectiveness of policies adopted by countries and their management strategy in dealing with Covid19 pandemic. Our model will enable political leaders to identify inadequate policies as quickly as possible and learn from their peers for more effective decisions. Coronaviruses are a large family of viruses that can cause a variety of conditions, from common cold to more severe diseases, such as acute respiratory syndromes. The first case was reported to the World Health Organization on 31 December 2019, cases of unknown pneumonia in Wuhan, China. 1 Since then, the city of Wuhan has taken unprecedented countermeasures against the outbreak, including closure of schools and business. Despite these measures, the spread of the disease was not contained, and the beast is spreading very fast, so far effecting 213 countries worldwide. As of 24 April 2020, there are 2,631,839 confirmed Covid19 cases and 182,100 deaths. 2 The world has experienced and struggled with numerous outbreaks and pandemics in the past, such as cholera, black plague, typhoid and influenza, but nothing like Covid19, disrupting every aspect of our lives. A sudden influx of patients to hospitals within a very short period of time, meant that even the most advanced healthcare systems around the world were unable to cope with the demand, and resources were stretched to its limit (e.g., beds, ventilators, personal protective equipment's, intensive care beds, doctors and nurses), forcing some countries to construct new Covid19 hospitals and departments within days and weeks. Covid19 has prompted the scientific community to research on a wide range of issues, the most pressing been the search for therapeutics and vaccines; modelling the spread of the disease and expected mortality; capacity planning of hospitals; the impact on supply chain logistics and the economy, and association of meteorological factors and disease spread. 3 However, no studies have been conducted to determine how efficient and effective countries are in terms of using scarce hospital resources to treat Covid19 patients, in the form of benchmarking, thus the opportunity of healthcare senior decision makers and political leaders to learn from best practices. They will be able to understand where their country and hospitals are heading in such a global disaster in terms of demand-capacity. Political leaders can determine the cities of their country which need to be considered more in terms of emergency action plan, specify how to optimize their limited resources with minimal loss. Advanced statistical modelling is typically carried out for benchmarking organisations/institutions, e.g., multilevel modelling. However, the main reasons to use DEA method are as follows: (1) 'DEA method enables to compare variables with "the best value" whereas statistical methods analyse the variables based on "average value" using central tendency approach', 4 and (2) There are multiple inputs and outputs, where DEA is the only methodology that is able to deal with such datasets. In this study, we developed a data envelopment analysis (DEA) based modelling to determine the efficiency levels of countries (out of the selected 16 nations) with respect to healthcare systems preparedness (in the form of availability of resources, i.e. number of physicians and hospital beds), demography (i.e. population, elderly people and age) and Covid19 confirmed cases. We collected weekly number of Covid19 cases for each country after 100th confirmed case, as significant increase is observed after 100th case, where intensive care units have reached intolerable levels. Note that the date that any country reaches the 100 th confirmed case is different. A total of 16 countries (See Table 1 ) is selected that had the outbreak for at least 5 consecutive weeks after their 100th confirmed case (as of 11 April 2020). DEA is an extremely popular technique applied to a wide range of sectors, including health, education, agriculture, finance and manufacturing services. 5 DEA gives a more meaningful index of comparative performance, establishing both the opportunities of improvement for healthcare services and reliable rankings for countries. DEA replaces multiple efficiency ratios by a single weighted sum of outputs over the weighted sum of inputs. The strength and ease of use of this method has attracted many researchers to develop models that rank hospital departments; compare healthcare services, and establish the efficiencies of clinics (i.e. surgery, gynaecology) in hospitals between countries 6-12 ). Using the DEA-based modelling approach, this study will enable healthcare organizations and governments to develop an effective strategy around resource planning and use (now and in the future) by comparing most effective nations in the fight against Covid19, and the opportunity to learn from those that are highly efficient. Furthermore, assessing weekly changes on efficiency levels of countries and better understanding whether the measures taken and implemented plans are sufficient or not, will enables us to assist organizations and governments to reallocate effectively and efficiently related resources (i.e., staff, beds, budgets), bring the regions struggling with the Covid19 outbreak under control, and allow us to debate the efficiencies of action plans against Covid19. The DEA and the super efficiency methodology are explained in Section 2; Section 3 presents the DEA-based modelling framework, where results are presented in Section 4, and finally Section 5 concludes the article. DEA is a method which is applied to determine the relative efficiencies of decision-making units (DMUs; e.g., hospitals, universities, countries) and has been widely used for a few decades. 13 15 The DEA model is a fractional programming which maximizes a ratio dividing the virtual outputs by virtual inputs. The weights are calculated by means of mathematical programming technique. 13 The fractional programming was converted to linear programming by Charnes et al. 16 The fractional programming of our DEA model for DMU 1 (i.e., United States) is adapted from Cooper et al. 17 by considering 16 DMUs, seven inputs and three outputs shown in Table 2 . The formulation of a typical DEA model is in Appendix. The objective function (1) maximizes the ratio dividing output by input. Constraint (Equation (2)) ensures each ratio for every DMU not to exceed 1. Constraints (3) and (4) are positive variables. 17 The linear programming (adapted from Cooper et al. 17 ) obtained from the fractional programming above is for DMU 1 in Appendix. The objective function (5) maximizes the weighted sum of outputs. Constraints (6) ensures the weighted sum of outputs is less than or equals to the weighted sum of inputs for every DMU. Constraints (7) and (8) (2) and (6)). Several DMUs (e.g., China) are determined as efficient DMU as can be seen in the next The data for a total of 222 regions (i.e., countries and continents) are recorded and the data we used in this study is from 31st December 2019 to 11th April 2020. The data were provided in .csv format from (Roser et al., 19 World Population Review 20 ) and imported into Microsoft SQL Server version 12.0. for database programming and analysis purposes. The data period depends on the data of 100th confirmed case within each country. After initial checks, we decided to include 16 countries in our study according to the criterion explained in the next section. Therefore, the starting dates of the first week for each country differ due to the dates of 100th occurring case, that is, 10th of March 2020 for USA and 29th of January for Japan. Table 1 shows the starting dates of the first week and the number of days since the 100th confirmed case. The hierarchy of the developed data envelopment analysis model for the Covid19 outbreak ORDU ET AL. -5 Table 2 gives the descriptive statistics of the input and output variables used in this study. The percentage of elderly people as well as median age is very high in Japan, whereas in Iran the opposite. There is a huge difference regarding total confirmed cases between the countries having minimum and maximum values, that is, the total confirmed cases in the United States is around six times the average value. The highest number of physicians per 1000 people in Norway and Japan uses around 13 beds per 100,000 people. The lowest values for these two inputs are observed in Iran. The highest number of deaths is observed to be in the United States, whereas UK with the highest mortality rate. The DEA-based modelling framework includes the five steps to determine weekly efficiencies of regions struggling with the pandemic outbreak. A total of 80 DEA model was developed, that is, 16 DEA models for each week. Table 3 and Figure 2 illustrate the efficiency scores of the DMUs for each week over the study period. Most countries have lost the ability to cope with Covid19 over time (from week 1 to week 5). While some countries (i.e. USA, UK, Germany and Belgium) in the first week were around the efficient boundary, they experienced a dramatic decrease in the following week. The efficiency scores gradually decreased in subsequent weeks. Countries (i.e. Spain, Italy, Switzerland and Netherlands) having low efficiency scores since the first week failed in the five-week period and their efficiency scores decreased further. Some of those countries with greater than one efficiency score (i.e. France, China, South Korea, Sweden and Norway) in the first week maintained their effectiveness and the rest experienced fluctuations. For example, China has become an efficient country consecutively during the 5-week period. As of May 2020, South Korea was still combatting with the disease and maintained efficiency for the entire study period, whereas Iran and Singapore were super-efficient countries. Table 4 summaries the DEA-based modelling approach. This study provides a periodical-based DEA modelling framework for healthcare organizations and governments to more proactively manage the Covid19 outbreak process. Thus, in order to overcome this process with less material and moral losses, these institutions and organizations will be provided to update themselves and renew their perspective. For example, the followed paths outbreak, it has been revealed that herd immunity policy did not work, and this policy could destroy health systems. As a result, the number and percentage of efficient DMUs in Table 4 proved that countries need different policies rather than the current to control the pandemic. With this DEA-based modelling approach, some results have been reached with this study, where the activities of countries in the fight against Covid19 are taken into consideration. For example, when the inputs of the study are examined, it is seen that Iran is disadvantaged in terms of health system preparedness, and despite its disadvantage, it has the highest efficiency. It can be said that the positive effects of the younger population and outbreak management increased the effectiveness. Although Japan's population average age is high, it has been observed that it is managing well, compared to other countries in terms of strength of its health system and policies they implemented during the pandemic. While the average number of healthcare workers in countries analysed within the scope of the study is 11.63 per 1000 people, this number in America is 9,73; the average bed capacity is 3.13 per 100,000 people, and 2.57 in USA. In all low-productivity countries, such as Spain, Italy, Netherlands and Switzerland, the average age of its population is above the average of the remaining countries in the study. However, they are the more developed countries having advanced and modern health systems. These low-efficient countries need to urgently change their outbreak management policies to turn their age disadvantage into an advantage. Abbreviations: DEA, data envelopment analysis; DMU, decision making unit. As can be seen from our inputs and outputs, the mortality rate and spread of the virus do not depend on a single factor. With the combination of a few negative factors, such as elderly population average, health conditions, and poor management policies, the efficiency decreases gradually. This study can guide the organizations on outbreak management policies and the analysis of the current situation of the countries. In addition, the results obtained from the study and especially the epidemic management policies of the countries with high effectiveness have been analysed and the basis has been established for the preparation of a guide for combating the Covid19 outbreak. We considered only 16 countries in this study due to the reasons explained in the previous sections. However, the study can be further extended by including more countries, longitudinal data considering more than 5 weeks, additional inputs (e.g., total number of tests, weekly number of tests, number of intensive care units) and output (e.g., total number of recovered patients and weekly number of recovered patients). We only used seven inputs and three outputs, and additional variables could have been considered, such as the number of tests for Covid19 per week. During the pandemic outbreak, researchers might have difficulties in accessing data, however, organizations such as WHO and/or governments can make relevant data available for research purposes. COVID-19) Outbreak. World Health Organization COVID-19) Dashboard. 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Max θ ¼ μ 1 y 11 þ μ 2 y 21 þ μ 3 y 31 ð5Þwhere, u s is the weight of output s, v i is the weight of input i, y sj is the value of output s for decision making unit (DMU) j, and x ij is the value of input i for DMU j, j = 1, 2, …, 16where, u s is the weight of output s, v i is the weight of input i, y s6 is the value of output s for DMU China, x i6 is the value of input i for DMU China.